Applying an action on a data item according to a classification and a data management policy

ABSTRACT

A classifier classifies data to identify a class of the data. Based on the identified class, at least one action to apply to the given data item is determined according to the data management policy, where the data management policy specifies a plurality of actions to apply for different classes of data items.

BACKGROUND

A user can have multiple electronic devices such as a desktop computer,notebook computer, tablet computer, smartphone, and so forth. Each ofthe electronic devices associated with a user can be used to access dataand make modifications to data, such as word processing documents,presentation documents, and so forth. In some cases, data of anelectronic device can be backed up to a separate storage system for dataprotection, or archived to the storage system to free up storage spaceat the electronic device.

BRIEF DESCRIPTION OF THE DRAWINGS

Some embodiments are described with respect to the following figures:

FIGS. 1 and 2 are block diagrams of example arrangements includingvarious devices according to some implementations;

FIG. 3 is a flow diagram of a data management process at an electronicdevice, according to some implementations;

FIG. 4 is a flow diagram of a data management process at a server,according to some implementations; and

FIG. 5 is a block diagram of a system according to some implementations.

DETAILED DESCRIPTION

It can be relatively difficult to manage data that exists on variouselectronic devices of a user. As an example, the user can edit adocument (e.g. word processing document, presentation document,spreadsheet, etc.) on a first electronic device, such as a desktopcomputer belonging to the user. Later, the user may travel to adifferent location where the user does not have access to the desktopcomputer, but the user may be carrying a mobile device such as a tabletcomputer, smartphone or notebook computer. In some cases, the latestversion of the document that exists on a desktop computer may not beavailable to the user when the user is away from the desktop computer.

In an enterprise environment (e.g. an environment of a business concern,educational organization, government agency, etc.), server systems canalso be provided to perform data management, such as to back up data,archive data, stub data, apply a data retention policy, apply a legalhold on data, and so forth. Backing up data of an electronic device at astorage system associated with a server system can allow for laterrecovery of data should a fault occur at the electronic device thatleads to data corruption or loss. Archiving data allows the data to bemoved from an electronic device for storage in a separate location toreduce consumption of storage resources at the electronic device.Stubbing data refers to removing a data item from an electronic deviceto a separate location while leaving a link (or “stub”) that can be usedto link to the data item at the separate location. The server system canapply a data retention policy to determine which data items to retainand which to delete. The server system can also apply a legal hold ondata for legal reasons, such as to satisfy discovery obligations inlitigation. There can be other data management actions that can beapplied on data, such as understanding the confidentiality of data,determining whether encryption should be applied, determining whetherdata retention should be limited or whether expedited destruction shouldbe applied. Other example data management actions include understandingthe frequency at which data is generated to better forecast data growth,and understanding related expenses relating to bandwidth and storageconsumption.

As users increasingly adopt multiple electronic devices, the managementof data at the multiple electronic devices associated with a given user(to allow ready access of data at any of the multiple electronic devicesfrom any other electronic device) in conjunction with performing variousdata management tasks at server systems can be challenging. Inaccordance with some implementations, to provide robust data managementin an environment where a relatively large number of users can each havemultiple electronic devices (e.g. desktop computers, notebook computers,tablet computers, smartphones, etc.), multiple functional features canbe integrated into each electronic device for managing data items at thecorresponding electronic device. Examples of data items can include wordprocessing documents, presentation slides, image files, video files,audio files, and so forth.

The multiple functional features of an electronic device can provide atleast some of the following: synchronizing data items among electronicdevices of a given user (or group of users, such as a family, adepartment within an enterprise, etc.), where the multiple electronicdevices of the user or group of users make up a private cloud of theuser; backing up data to a remote storage system; archiving data to aremote storage system; stubbing data; applying a data retention policywith respect to the data; applying a legal hold on data; sharing thedata of a first user with another user; compressing data; encryptingdata; and so forth. The multiple functional features associated withdata management that are integrated into the electronic device allow forvarious data management actions (any of the foregoing or any otheraction) to be selectively applied to data items, based on classificationof the data items as discussed below. Note that making a decision toskip performing a data management action is also considered an actionthat can be selectively taken by the functional features of theelectronic device.

In some implementations, a classifier can also be included in anelectronic device for classifying a data item. Classifying a data itemincludes identifying a type (or class) of a data item based oninformation associated with a data item. The classification of a dataitem can be used to determine a sensitivity level of the data item, aconfidentiality level of the data item, and so forth, which can be usedto determine selected management action(s) to apply. In some examples,the classification can be based on content in the data item. In otherexamples, classification can be based on metadata associated with a dataitem, such as the file name of the data item, the file extension of thedata item, and so forth. In further implementations, a data item canalso be classified according to the type of electronic device (e.g.desktop computer, notebook computer, tablet computer, smartphone, etc.)where the data item resides or was created.

The electronic device can also maintain a predetermined data managementpolicy. The data management policy can specify action(s) to be takenwith respect to a data item based on the classification applied to thedata item.

In accordance with some implementations, an integrated data managementmechanism or technique is implemented by providing various functionalfeatures (such as those discussed above) in each electronic device,where selected actions can be applied by invoking any one or multiple ofthe functional features based on classification of each data itemperformed by the classifier and based on the data management policymaintained at the electronic device. In this manner, data accessibilityis improved (e.g. a user can readily access a data item at any of theelectronic devices in a private cloud, or a user can easily share datawith other users to perform collaboration), and various data managementfunctionalities offered by a data manager server system are also readilyavailable. The server 120 can be implemented as a server computer, or acollection of server computers. The server 120 is coupled to a storagesystem 122, which can be implemented with a storage device or acollection of storage devices.

FIG. 1 is a block diagram of an example arrangement that includeselectronic devices 102 and 104, which are coupled to a server 120, suchas over a data network.

The electronic devices 102 and 104 can belong to the same user—in thisscenario, the electronic devices 102 and 104 are part of a private cloudof the user. In other examples, electronic devices 102 and 104 canbelong to different users. Although reference is made to a private cloudof the user, it is noted that techniques according to someimplementations can also be applied in the context of a multi-tenantpublic cloud, which has multiple tenants that are able to accessfunctionality of the public cloud, or in the context of a hybrid cloudthat includes both a private cloud and public cloud.

The electronic devices 102 and 104 can include similar components, whichare labeled with the same reference numerals in FIG. 1. The electronicdevice (102 or 104) can include a data manager 106, which is to performa policy-driven action (or actions) with respect to a data item 108based on a classification applied by a classifier 110 on the data item108. The classification produces classification-derived information,which is used to drive the policy-driven data management that is basedon a data management policy 112 maintained in the electronic device,where the data management policy 112 can be user-specified.

The classifier 110 classifies the data item 108 into a selected one ofmultiple types (or classes) of the data item. Based on theclassification, various characteristics of the data item can bedetermined, including a sensitivity level of the data item, aconfidentiality level of the data item, a context associated with thedata item, and so forth. As examples, the data item can be classified asbeing a confidential document, a multimedia file such as a music file orvideo file, an old document that has an age past a predefined agethreshold, a legal document that is subject to a legal hold policy, acollaboration document that is to be collaborated among multiple users,a document that is to be synchronized among multiple devices in aprivate cloud of a given user, and so forth. The classification of thedata item 108 can also be based on the type of electronic device atwhich the data item 108 resides or was created. Based on theclassification applied on the data item 108, the data manager 106 caninvoke at least one of multiple functional features 114 that applycorresponding actions on the data item 108. Examples of variousfunctional features 114 are listed above. Note that plural ones of thefunctional features can be invoked to perform respective pluralmanagement actions with respect to the data item, based on theclassification of the data item. As another example, note that apossible action that can be taken by the data manager 106 based on theclassification of the data item 108 and the data management policy 112is a decision to skip the invocation of any of the functional features114.

In a specific example, if the classifier 110 classifies the data item108 as being a data item that is to be synchronized in a private cloud,the data manager 106 can invoke a synchronization feature (one of thefunctional features 114) for synchronizing the data item 108 amongmultiple electronic devices in the private cloud of the user.Synchronizing data among multiple electronic devices (in the privatecloud) of a given user (or group of users) refers to maintainingsynchronization of data items as they are being modified. A change in afirst data item at a first electronic device in the private cloud can becommunicated to the other electronic device(s) of the private cloud,such that the change can also be applied to a version of the first dataitem that may be kept at the other electronic device(s). Suchsynchronized data in the private cloud of the user is readily accessibleby the user regardless of the electronic device the user is using orwhere the user is located.

As a further example, based on the classification, multiple managementactions can be performed, including data synchronization as well as anyone or multiple of the following: encrypt the data item 108 because thedata item was identified as being confidential; notifying legalpersonnel to review the data item because the data item was identifiedas one that may be impacted by a litigation; creating an audit trailbased on the notification, and so forth. Note that the notification andaudit trail creation are examples of ancillary actions that are notapplied on the data item, but are performed in addition to actions onthe data item.

As another example, the data item 108 can be classified as a music file.In this case, the data management policy 112 can specify thatcompression and encryption (which can be provided by respectivefunctional features 114) are not to be applied to the music file.However, a backup feature (one of the functional features 114) isinvoked by the data manager 106 to back up the music file to a remotestorage system, such as the storage system 122 coupled to a server 120.

As a further example, the data item 108 may be classified as one thatmay be subject to potential copyright protection. In this example, themanagement action specified can be a decision to avoid backup of thedata item 108 to avoid copyright violations. Additionally, a notice(s)can be sent to a user (and compliance personnel) to indicate thatcertain material may be associated with copyright protection issues, andan audit trail can be created that contains a record of the notice(s).

As another example, the classifier 110 can classify the data item 108 asbeing a data item that is to be archived, such as in cases where thedata item has an age that is greater than some predefined age threshold,or where the data item satisfies another predefined archival criterion.In this case, the data management policy 112 can specify that the dataitem to be archived is to be removed from the electronic device andmoved to the remote storage system 122 for storage. Archiving can beperformed in conjunction with stubbing, where a link to the removed dataitem can be kept at the electronic device. The link can be used at theelectronic device to access the data item at the remote storage system122. From the perspective of a user of the electronic device, it appearsthat the data item is still resident at the electronic device, and canbe accessed on demand.

Stubbing can also be employed in the context of data restoration. Forexample, data determined to be most immediately useful to the user canbe restored first, while the rest of the data can be restored as stubs.A determination of what is immediately useful can be based on any one orcombination of the following, in some examples: classification derivedinformation, frequency of use, or recency of use.

Data corresponding to the stubs can be gradually restored in thebackground, with the restored data replacing the corresponding stubs.This “intelligent” restoration approach can allow the user to get up andrunning more quickly by first providing the user immediate access tomore important data, with access to the remaining (less important) dataprovided with slight delay.

As another example, the classifier 110 can classify the data item 108 asbeing of a type that is subject to a hold, such as for legal reasons. Inthis case, the data management policy 112 can specify that such a dataitem that is subject to a legal hold is to be backed up to the storagesystem 122 to prevent loss of this data item. Also, metadata can beassociated with the data item 108 that is subject to the legal hold toprevent deletion of this data item.

As another example, the classifier 110 can classify the data item 108 asbeing a collaborative data item, in which case the data managementpolicy 112 can specify that the data item is to be shared with anyelectronic device of another user(s).

Other examples relating to classification of the data item 108 andcorresponding action(s) to be taken based on the data management policy112 are also possible.

FIG. 2 is a block diagram of an example arrangement according to furtherimplementations. The components of the electronic device 102 that arethe same as those depicted in FIG. 1 are assigned the same referencenumerals. In the FIG. 2 example, the data manager 106 is depicted tohave specific functional features, including the following: a compressfeature (to compress a data item); an encrypt feature (to encrypt a dataitem); a stub feature (to remove a data item having a predeterminedcharacteristic from the electronic device 102 to store in the storagesystem 122, and to include a link to the removed data item at theelectronic device 102); a hold feature (to apply a hold policy to a dataitem, such as for a legal reason); a sync feature (to synchronize a dataitem among electronic devices in a private cloud of a user); a sharefeature (to share a data item between users); a delete feature (todelete data items according to a data retention criterion); a backupfeature (to back up a data item to the storage system 122); and anextensible feature (to extend functionality of the data manager 106).

Note that the compress feature can apply compression on a data item thatcan selectively use any one of multiple compression approaches. Forexample, a selected compression approach may be based on the data item'scontent and resource utilization goals (e.g. processing resources usedfor compression should not exceed a target number of processors or atarget processing time).

The extensible feature of the data manager 106 can be used to addadditional functional features to the data manager 106, such as by useof plug-in modules or by some other mechanism.

The electronic device 102 of FIG. 2 also includes a deduplication engine202, which can be used to apply deduplication of data. In performingdeduplication, the deduplication engine 202 can identify a first portionof a data item that is already available at the storage system 122, anda second portion of the data item that is not available at the storagesystem 122 (or at another location). As part of the deduplication, thededuplication engine 202 avoids sending the first portion (since it isalready available at the storage system 122 or at another location), butinstead sends just the second portion to the server 120 for storing atthe storage system 122 (or at another location).

In some examples, deduplication can be based on chunking a data item,where the data item is divided into chunks. The deduplication engine 202can identify chunks of the data item that are already available at thestorage system 122 or at another location, and thus can avoid sendingsuch chunks again to the server 120. Instead, the deduplication engine202 can send just chunks of the data item 108 that are not alreadyavailable at the storage system 122 or at another location. In this way,each chunk of a data item is stored just once. Note that common chunkscan actually be shared among multiple data items—deduplication wouldalso cause such chunks that are shared among multiple data items to bestored just once at the storage system 122 or at another location. Inother implementations, other deduplication techniques can be used.

In addition to the various data management functionalities in eachelectronic device, the server 102 can also include data managementfunctionalities that can cooperate with, or supplement, the datamanagement features of the electronic device.

The server 120 of FIG. 2 includes a data manager 204, which can includesimilar functional features as the data manager 106 in the electronicdevice 102. The server 120 also includes a data management policy 206(which can be user-specified), which specifies a policy to be applied bythe data manager 204 on a data item being processed by the server 120.

Scheduled jobs 208 in the server 120 refer to jobs relating toprocessing of data items in response to data provided by the electronicdevices 102 and 104 to the server 120.

Note that data received from an electronic device can be partial data,due to the duplication applied by the deduplication engine 202 in theelectronic device. As a result, a delta application module 210 isprovided in the server 120 to reproduce an original data item frompartial data that is received from an electronic device 102. As anexample, an original data item at the electronic device may have tenchunks. Due to deduplication, only 4 out of the 10 chunks may have beensent by the electronic device to the server 124 for handling. The deltaapplication module 210 in the server 120 can recreate the original dataitem from the 4 chunks, by pulling the remaining 6 chunks from thestorage system 122 (or from another location).

The recreated data item is then provided to a “deep” classifier 212 inthe server 120. The deep classifier 212 can apply a deeperclassification on the recreated data item than applied by the classifier110 at an electronic device. For example, the classifier 110 at anelectronic device may apply classification of a data item based onmetadata of the data item and based on a portion of content in the dataitem. The deep classifier 212 can apply further classification based onthe entirety of the content of the data item (as well as the metadataassociated with the data item). Performing deep classification at theserver 120 allows data management techniques according to someimplementations to leverage greater processing resources available atthe server 120, while reducing processing resource consumption at clientdevices. Note that the processing resources of the server 120 are morescalable than processing resources at the client devices, sinceadditional processing resources can be dynamically added to the server120 on demand in some examples. Performing the deep classification atthe server 120 also allows for re-processing of data already collectedthat the user has chosen to not classify, or after newer or betterclassifiers or classification rules are made available (in other words,the classification technique has been updated).

Based on the classification applied by the deep classifier 212 on a dataitem, the data manager 204 can perform a policy-driven action accordingto the data management policy 206 at the server 120, which can includeactions such as compression, encryption, archiving, stubbing, holding,synchronization, sharing, deletion, notification, auditing, otherancillary actions that go beyond actions on the data item, and so forth.

To the extent that the action performed by the data manager 204 involvesstoring data to or retrieving data from the storage system 122, the datamanager 204 is able to communicate such data with the storage system122. Also for purposes of synchronization, the data manager 204 cancause a data item received from a first electronic device to becommunicated to one or multiple other electronic devices.

The server 120 also includes a classification-enriched search engine214, which can perform a search for data (in the storage system 122 orin another location such as any of the electronic devices 102 and 104).The classification-enriched search engine 214 is able to useclassification information provided by the deep classifier 212 to finddata items in the storage system 122 and/or other storage locations thatsatisfy search criteria in a search request and that are related to aparticular class (or classes) identified by the classifier 212. Forexample, the classification-enriched search engine 214 can find dataitems for which a legal hold applies and which satisfy a searchcriterion or search criteria in a search request. As a further example,the classification-enriched search engine 214 can perform searching forthe purpose of understanding data growth, and to provide the ability tomanage relatively large amounts of data for efficient but accurateglobal actions such as authorization of the deletion of data that isdeemed eligible for deletion using heat maps and other cluster-basedvisualizations of data.

The server 120 also includes an outflow module 216, which provides aquery interface, such as an application programming interface, to allowinteraction between the classification-enriched search engine 214 and adiscovery tool 218. The discovery tool 218 can be used to manage dataitems for litigation, such as data items that are responsive tolitigation-related document requests for production, or data itemsspecified by court order or rules. The outflow module 216 can restore aspecific version of a data item from the storage system 122, can apply adelta operation to recreate a full data item from partial data (due todeduplication), and so forth. The discovery tool 218 can view, classify,apply legal hold (by copying), or perform other tasks with respect toretrieved data items. This can allow third party access of the retrieveddata items in some examples.

As further depicted in FIG. 2, the storage system 122 can include astorage abstraction layer 220 to abstract an interface that is presentedto the server 120. In addition, the storage system 122 can include anear-term storage medium 222 (implemented with one or multiple storagedevices) and a long-term storage medium 224 (implemented with one ormultiple storage devices). Generally, the storage device(s) of thenear-term storage medium 222 can be more expensive (and have higherperformance with respect to one or multiple performance metrics) thanthe storage device(s) of the long-term storage medium 224.

In some examples, the near-term storage medium 222 can be used to storethe following types of data items: data items synchronized in a privatecloud (such as different versions of each such synchronized data item);data items to be shared among users (such as different versions of eachsuch shared data item); backed up data items; and so forth. Thelong-term storage medium 224 can be used to store archived data items,for example.

The storage system 122 can also store metadata 226 associated with thedata items handled by the server 120. For example, the metadata 226 caninclude indications of classifications of each corresponding data item,information specifying that a legal hold is to be applied to acorresponding data item, and so forth.

FIG. 3 is a flow diagram of a data management process 300 according tosome implementations. The data management process 300 can be performedby components in any electronic device, such as electronic device 102 or104 in FIG. 1 or 2. The electronic device receives (at 302) a given dataitem (such as data item 108 in FIG. 1 or 2). The classifier 110 in theelectronic device classifies (at 304) the given data item to identify aclass of the given data item (from among the multiple classes). Based onthe identified class, the data manager 106 determines (at 306) at leastone action to apply according to the data management policy 112. Theaction to apply can be selected from among the actions associated withthe various functional features depicted in FIG. 1 or 2. In some cases,the selected action can be a decision to skip invoking any of thefunctional features in the electronic device.

FIG. 4 is a flow diagram of a data management process 400 performed atthe server 120 of FIG. 1 or 2. The server 120 receives (at 402) datafrom an electronic device, where the received data can be partial data(due to deduplication applied at the electronic device). If the receiveddata is partial data, then the delta application module 210 of theserver 120 recreates (at 404) a full data item from the partial data.For example, if the received partial data includes a subset of thechunks of the full data item, the delta application module 210 is ableto retrieve the remaining chunks (such as from the storage system 122)to combine with the received chunks to form the full data item.

The deep classifier 212 in the server 120 then applies (at 406) deepclassification on the recreated data item, to identify a class of therecreated data item. Based on the classification, the data manager 204in the server 120 determines (at 408) at least one action to apply tothe recreated data item according to the data management policy 206 inthe server. The action(s) can include various actions similar to thosein the electronic device, except that the selected action in the server120 is based on the deep classification.

FIG. 5 depicts a system 500, which either be the electronic device 102or 104, or the server of FIG. 1 or 2. The system 500 includes datamanagement machine-readable instructions 502, which can be any of thevarious modules depicted in FIG. 1 or 2 that are in the electronicdevice or server. The data management machine-readable instructions 502are loaded for execution on a processor or processors 504. A processorcan include a microprocessor, microcontroller, processor module orsubsystem, programmable integrated circuit, programmable gate array, oranother control or computing device.

The processor(s) 504 can be coupled to a network interface 506 (to allowthe system 500 to perform communications over a data network) and astorage medium (or storage media) 508.

The storage medium or storage media 508 can be implemented as one ormultiple computer-readable or machine-readable storage media. Thestorage media include different forms of memory including semiconductormemory devices such as dynamic or static random access memories (DRAMsor SRAMs), erasable and programmable read-only memories (EPROMs),electrically erasable and programmable read-only memories (EEPROMs) andflash memories; magnetic disks such as fixed, floppy and removabledisks; other magnetic media including tape; optical media such ascompact disks (CDs) or digital video disks (DVDs); or other types ofstorage devices. Note that the instructions discussed above can beprovided on one computer-readable or machine-readable storage medium, oralternatively, can be provided on multiple computer-readable ormachine-readable storage media distributed in a large system havingpossibly plural nodes. Such computer-readable or machine-readablestorage medium or media is (are) considered to be part of an article (orarticle of manufacture). An article or article of manufacture can referto any manufactured single component or multiple components. The storagemedium or media can be located either in the machine running themachine-readable instructions, or located at a remote site from whichmachine-readable instructions can be downloaded over a network forexecution.

In the foregoing description, numerous details are set forth to providean understanding of the subject disclosed herein. However,implementations may be practiced without some or all of these details.Other implementations may include modifications and variations from thedetails discussed above. It is intended that the appended claims coversuch modifications and variations.

What is claimed is:
 1. A method comprising: receiving, by a particularelectronic device of a private cloud of electronic devices belonging toa user, a given data item; classifying, by the particular electronicdevice, the given data item to determine a class of the given data item;based on the classifying, determining, by the particular electronicdevice, at least one action to apply to the given data item according toa predetermined data management policy specifying a plurality of actionsto apply for different classes of data items, the actions includingsynchronizing a data item with another electronic device of the privatecloud, placing a hold on a data item, and sharing a data item amongusers; applying, by the particular electronic device, deduplication onthe given data item to determine a first portion of the given data itemthat is already available at a server, and a second portion that is notalready available at the server; and sending, by the particularelectronic device, the second portion of the given data item to theserver for handling, without sending the first portion, wherein thehandling includes applying, by the server, classification of the givendata item at a deeper level based on content of the given data item notconsidered by the classifying by the particular electronic device, andbased on the classification at the deeper level, the server determiningat least one further action to apply to the given data item according toa predetermined data management policy at the server.
 2. The method ofclaim 1, wherein the plurality of actions further include backing up adata item to a remote storage location or archiving a data item based ona characteristic of the data item.
 3. The method of claim 1, wherein theplurality of actions further include stubbing a data item by removingthe data item from the particular electronic device, storing the removeddata item at a remote storage location, and adding a link to the removeddata item at the particular electronic device.
 4. The method of claim 1,wherein the plurality of actions further include deleting a data itemaccording to a data retention criterion.
 5. The method of claim 1,wherein the plurality of actions further include compressing a data itemand encrypting a data item.
 6. The method of claim 1, wherein thepredetermined data management policy that specifies the plurality ofactions further specifies performance of an ancillary action that is inaddition to the at least one action applied to the given data, andwherein the method further comprises: performing the ancillary actionbased on the classifying.
 7. The method of claim 1, wherein the at leastone action includes synchronizing the given data item by the particularelectronic device with the at least another electronic device, theparticular electronic device and the at least another electronic devicebelonging to the user.
 8. The method of claim 1, wherein the pluralityof actions are applied by invoking respective functional features in theparticular electronic device, and wherein the applied at least oneaction includes deciding to skip invoking any of the functional featuresbased on the classifying and according to the predetermined datamanagement policy.
 9. The method of claim 1, further comprising:performing restoration of data items at the particular electronicdevice, wherein the restoration includes first restoring a subset of thedata items and restoring stubs corresponding to a remainder of the dataitems, wherein the restoration further includes gradually restoring theremainder of the data items corresponding to the stubs after firstrestoring the subset of the data items.
 10. An article comprising atleast one non-transitory machine-readable storage medium storinginstructions that upon execution cause a server to: receive data from anelectronic device; apply classification, by a classifier, on the data toidentify a class of the data, wherein the applied classification at theserver is different from data classification on the data performed atthe electronic device; and based on the classification, determine atleast one action to apply to the data according to a predetermined datamanagement policy, where the predetermined data management policyspecifies a plurality of actions to apply for different classes of data,the plurality of actions including synchronizing data among electronicdevices in a private cloud, and sharing data among users.
 11. Thearticle of claim 10, wherein the received data is partial data due todeduplication applied at the electronic device, and wherein theinstructions upon execution cause the server to further: recreate a dataitem from the partial data, wherein applying the classification on thedata comprises applying the classification on the data item, and whereinthe classification on the data item is based on content of the data itemnot considered by the data classification of the data item performed atthe electronic device.
 12. The article of claim 10, wherein theinstructions upon execution cause the server to perform aclassification-enriched search in response to a search request andaccording to the classification by the classifier.
 13. The article ofclaim 12, wherein the instructions upon execution cause the server tointeract with a legal discovery tool that manages data items forlitigation.
 14. The article of claim 10, wherein the plurality ofactions further include compressing the received data, where thecompressing selectively uses one of multiple compression approaches, theselecting based on content of the received data and a resourceutilization goal.
 15. The article of claim 10, wherein theclassification applied at the server allows re-processing of the datafor performing classification in a scenario where classification wasskipped at the electronic device, or in a scenario where aclassification technique has been updated since a classificationperformed on the data at the electronic device.
 16. The article of claim10, wherein the electronic devices in the private cloud belong to anindividual user.
 17. A server comprising: a storage medium to store adata management policy; at least one processor to receive data from anelectronic device; a classifier executable on the at least one processorto classify a given data item derived from the received data to identifya class of the given data item, wherein the classifying is a deeperclassification of the given data item than a classification of the givendata item performed at the electronic device, the deeper classificationconsidering content of the given data item not considered by theclassification of the given data item performed at the electronicdevice; and a data manager executable on the at least one processor to:based on the identified class, determine at least one action to apply tothe given data item according to the data management policy, where thedata management policy specifies a plurality of actions to apply fordifferent classes of data items, the plurality of actions includingsynchronizing a data item with at least another electronic device in aprivate cloud of a user, backing up a data item, and sharing a data itemamong users.
 18. The server of claim 17, further comprising a pluralityof functional features invocable by the data manager, where the selectedat least one action includes invocation of at least one of the pluralityof functional features, or a decision to skip invocation of thefunctional features.
 19. The server of claim 17, wherein the receiveddata is partial data due to deduplication at the electronic device, andthe server further comprises a data replication module to recreate thegiven data item from the partial data.